Learning Guide Draft

library(tidyverse)     # for data cleaning and plotting
library(gplots)        # for col2hex() function
library(RColorBrewer)  # for color palettes
library(sf)            # for working with spatial data
library(leaflet)       # for highly customizable mapping
library(carData)       # for Minneapolis police stops data
library(tidytuesdayR)  # for bigfoot data 
library(ggthemes)      # for more themes (including theme_map())
library(htmltools)
theme_set(theme_minimal())

Basemaps

We are gonna start from the bottokm layer up. The first thing to look it is the basemaps. This means, what will be behind your points or shapes. There are many options and they are super easy to load in. We can start off with the basic basic map, simpple blue ocean and white land, if you zoom in there will be more detials

leaflet() %>% 
  addTiles()

We can also start the map zoomed in on a specific area using latitude and longitude and the setView() function

Here is Saint Paul MN, now look up your hometown and practice puting in the coordinates.

leaflet() %>% 
  addTiles() %>% 
  setView(lng = -93.093124, lat = 44.949642, zoom = 12)

hometown <- leaflet() %>% 
  setView(lng = -93.093124, lat = 44.949642, zoom = 12) %>% 
    addTiles()

There are many other basemaps you can use in leaflet! To get different basemaps use the function addProviderTiles(), you will need to know the name of the basemap Here are some examples:

hometown %>% 
    addProviderTiles(providers$CartoDB.Positron)

hometown %>% 
    addProviderTiles(providers$Stamen.Watercolor)

hometown %>% 
    addProviderTiles(providers$CartoDB.DarkMatterNoLabels)

To get the full list of basemaps available through this function click here: http://leaflet-extras.github.io/leaflet-providers/preview/index.html

Let say you really like the water color basemap but there arent any labels, you can layer base maps. Here I have layed the water color with the light grey basemap that had labels. As long as you set the one on top to have a lower opacity you can see both!

hometown %>% addProviderTiles(providers$Stamen.Watercolor) %>%
  addProviderTiles(providers$CartoDB.Positron,
    options = providerTileOptions(opacity = 0.5)) 

Markers

Now we have our basemap figured out whe can add markers. To do that we will needed data and the observation should have a latitude and longitdue variable to put them on the map.

tuesdata <- tidytuesdayR::tt_load('2022-09-13') #Loading in data from a tidy tuesday that has geographical points

## 
##  Downloading file 1 of 1: `bigfoot.csv`

bigfoot <- tuesdata$bigfoot
bigfoot

leaflet(data = bigfoot) %>% addTiles() %>%
  addMarkers(~longitude, ~latitude)

If R is running slowly now its probably becuase there are so many data points. It is best to filter out observations that you need before making a map.

bigfootsub <- bigfoot %>% 
  filter(season == "Summer")

leaflet(data = bigfootsub) %>% addTiles() %>%
  addMarkers(~longitude, ~latitude)

With less points the software runs far smoother. Now we can add some fun things!

Plain Markers and Popups

Not only can you add labels to these points but with leaflet you can add popups

leaflet(data = bigfootsub) %>% 
  addProviderTiles(providers$CartoDB.Positron) %>% #Changing basemap to something more neutral 
  addMarkers(~longitude, ~latitude,label = ~date, popup = ~location_details) # label means what will appear when you hover over the point and popup means what will appear when you click on a point

Awesome Markers

Awesome markers allow you to change the color of the marker dependent on a variable

# After choosing temperature_high to be my variable I am visualizing I am going to filter out any observations that do not have a value for temperature_high
bftemp <- bigfootsub %>% 
  filter(temperature_high != "NA")
bftemp

#Then we will need to assign values of temperature_high  to colors creating a getColor function 

getColor <- function(bftemp) {
  sapply(bftemp$temperature_high, function(temperature_high){
  if(temperature_high <= 68) { 
    "blue"
  } else if(temperature_high >= 69) {
    "red"
  } else {
    "green"
  } })
}

icons <- awesomeIcons(
  icon = 'ios-close',
  iconColor = 'pink', #Controls color of middle x 
  library = 'ion',
  markerColor = getColor(bftemp) #Calls the function 
)

leaflet(bftemp) %>% 
  addProviderTiles(providers$CartoDB.Positron) %>% 
  addAwesomeMarkers(~longitude, ~latitude,label = ~date, popup = ~location_details, icon = icons) #make sure to set the icons

Color

# load continuous dataset
data_site <- 
  "https://www.macalester.edu/~dshuman1/data/112/2014-Q4-Trips-History-Data.rds" 
Trips <- readRDS(gzcon(url(data_site)))
Stations<-read_csv("http://www.macalester.edu/~dshuman1/data/112/DC-Stations.csv")

departSta <- Trips %>%
  left_join(Stations, by = c("sstation" = "name")) %>%
  group_by(lat, long) %>%
  summarise(EventsCount = n())

Create Color Palette

# Call the color function (colorNumeric) to create a new palette function
pal <- colorNumeric(c("red", "green", "blue"), 1:10)
# Pass the palette function a data vector to get the corresponding colors
pal(c(1,6,9))

## [1] "#FF0000" "#52E74B" "#6854D8"

# create another color palette function with the range of inputs (i.e. domain) 
palDomain <- colorNumeric(
  palette = "Blues",
  domain = departSta$EventsCount)
# Show the corresponding colors
head(palDomain(departSta$EventsCount))

## [1] "#F5FAFE" "#EFF6FC" "#F2F8FD" "#F5F9FE" "#EEF5FC" "#EFF6FC"

Common parameters

#RColorBrewer 
palBre <- colorNumeric(
  palette = "RdYlBu",
  domain = departSta$EventsCount)

#viridis
palVir <- colorNumeric(
  palette = "magma",
  domain = departSta$EventsCount)

#RGB or Named the colors: palette(), c("#000000", "#0000FF", "#FFFFFF"), topo.colors(10) etc

#A function that receives a single value between 0 and 1 and returns a color: colorRamp(c("#000000", "#FFFFFF"), interpolate="spline") etc

Continuous data

#Continuous input, continuous colors (colorNumeric)
palConC <- colorNumeric(
  palette = "RdYlBu",
  domain = departSta$EventsCount)

#Continuous input, discrete colors (colorBin and colorQuantile)

# colorBin:slicing the input domain up by value(bin)
palBin<-colorBin("Blues", departSta$EventsCount, 5, pretty = FALSE)

#colorQuantile: slicing the input domain into subsets with equal numbers of observations (by quantile)
palQuan <- colorQuantile("Blues", departSta$EventsCount, n = 7)

# leaflet(data = departSta) %>%
#   addProviderTiles(providers$CartoDB.DarkMatter) %>%
#   addProviderTiles(providers$Stamen.TonerLines,
#                    options = providerTileOptions(opacity = 0.35)) %>%
#   addProviderTiles(providers$Stamen.TonerLabels) %>%
#     addCircles(lng = ~long,
#              lat = ~lat,
#             #stroke width in pixels
#              weight = 10,
#             #changes transparency, like alpha in ggplot
#              opacity = 1,
#              color = ~palQuan(EventsCount))

categorical data

#Domain
palFacD<-colorFactor(palette = "Blues", MplsStops$problem)
#Level
palFacL<-colorFactor(topo.colors(5),levels = MplsStops$problem)

# leaflet(data = MplsStops) %>%
#   addProviderTiles(providers$CartoDB.Positron) %>%
#   addCircleMarkers(lng = ~long,
#              lat = ~lat,
#              weight = 1,
#              opacity = 1,
#              stroke = TRUE,
#              color = ~palFacL(problem))

Lines and Shape

#rectangle
leaflet(data = departSta) %>%
  addProviderTiles(providers$CartoDB.DarkMatter) %>%
  addProviderTiles(providers$Stamen.TonerLines,
                   options = providerTileOptions(opacity = 0.35)) %>%
  addProviderTiles(providers$Stamen.TonerLabels) %>%
  addCircles(
    lng = ~ long,
    lat = ~ lat,
    #stroke width in pixels
    weight = 10,
    #changes transparency, like alpha in ggplot
    opacity = 1,
    color = ~ palQuan(EventsCount)
  ) %>%
  addRectangles(
    lng1 = -77.20250,
    lat1 =38.80111,
    lng2 =-76.93186,
    lat2 = 39.12351,
    fillColor = "transparent"
  )

#Polygons and Polylines
leaflet(data = departSta) %>%
  addProviderTiles(providers$CartoDB.DarkMatter) %>%
  addProviderTiles(providers$Stamen.TonerLines,
                   options = providerTileOptions(opacity = 0.35)) %>%
  addProviderTiles(providers$Stamen.TonerLabels) %>%
  addPolygons(
    lng = ~ long,
    lat = ~ lat,
    # set the opacity of the outline
    opacity = 1,
    # set the stroke width in pixels
    weight = 1,
    # set the fill opacity
    fillOpacity = 0.6
  )

Legend

leaflet(data = MplsStops) %>%
  addProviderTiles(providers$CartoDB.Positron) %>%
  addCircleMarkers(lng = ~long,
             lat = ~lat,
             weight = 1,
             opacity = 1,
             stroke = TRUE,
             color = ~palFacL(problem)) %>% 
    addLegend(position = "bottomleft", 
            pal = palFacL,
            values = ~problem,
             title = "Type of Stops")